"""Async background worker for processing ingestion jobs."""

import asyncio
import hashlib
import json
import logging
import shutil
from pathlib import Path

from kb import config, database, embeddings, staging
from kb.database import build_enriched_text
from kb.ingest import detector

logger = logging.getLogger("kb.worker")


async def ingestion_worker() -> None:
    """Main background loop that processes queued ingestion jobs.

    Runs indefinitely until cancelled.  Every 2 seconds it checks for the
    oldest queued job, marks it as *processing*, and delegates to
    :func:`_process_job` in a thread pool.
    """
    logger.info("Ingestion worker started")
    while True:
        try:
            cfg = config.cfg
            conn = database.get_connection(cfg.db_path)
            try:
                row = conn.execute(
                    "SELECT * FROM jobs WHERE status = 'queued' "
                    "ORDER BY created_at ASC LIMIT 1"
                ).fetchone()
                if row is None:
                    await asyncio.sleep(2)
                    continue

                job_id = row["id"]
                database.update_job_status(conn, job_id, "processing")
                logger.info("Processing job %d (%s)", job_id, row["filename"])
            finally:
                conn.close()

            try:
                status, doc_id, chunk_count = await asyncio.to_thread(
                    _process_job, row
                )
            except Exception as exc:
                logger.exception("Job %d failed", job_id)
                conn = database.get_connection(cfg.db_path)
                try:
                    database.update_job_status(
                        conn, job_id, "failed", error=str(exc)
                    )
                finally:
                    conn.close()
                continue

            conn = database.get_connection(cfg.db_path)
            try:
                database.update_job_status(
                    conn,
                    job_id,
                    status,
                    document_id=doc_id,
                    chunk_count=chunk_count,
                )
            finally:
                conn.close()

            logger.info(
                "Job %d finished: status=%s doc_id=%s chunks=%s",
                job_id, status, doc_id, chunk_count,
            )

        except asyncio.CancelledError:
            logger.info("Ingestion worker cancelled")
            raise
        except Exception:
            logger.exception("Unexpected error in ingestion worker loop")
            await asyncio.sleep(2)


def _process_job(job_row) -> tuple[str, int | None, int]:
    """Synchronously process a single ingestion job.

    Returns:
        A tuple of ``(status, document_id, chunk_count)`` where *status* is
        one of ``"done"``, ``"skipped"``.
    """
    cfg = config.cfg
    conn = database.get_connection(cfg.db_path)
    staged_path = Path(job_row["staging_path"])

    try:
        # --- Determine document type and language -------------------------
        filename = job_row["filename"]
        forced_type = job_row["doc_type"]

        if staged_path.suffix == ".note":
            doc_type = "note"
            language = None
        elif forced_type:
            doc_type = forced_type
            language = None
        else:
            doc_type, language = detector.detect_type(Path(filename))

        # --- Chunk the content --------------------------------------------
        if doc_type == "note":
            text = staged_path.read_text(encoding="utf-8")
            from kb.ingest.note import chunk_note
            chunks = chunk_note(text)
        elif doc_type == "pdf":
            from kb.ingest.docling_pipeline import chunk_document
            chunks = chunk_document(staged_path, cfg.ingest_device)
        elif doc_type == "markdown":
            text = staged_path.read_text(encoding="utf-8")
            from kb.ingest.markdown import chunk_markdown
            chunks = chunk_markdown(text)
        elif doc_type == "code":
            text = staged_path.read_text(encoding="utf-8")
            if not language:
                _, language = detector.detect_type(Path(filename))
            from kb.ingest.code import chunk_code
            chunks = chunk_code(text, language)
        else:
            raise ValueError(f"Unsupported doc_type: {doc_type}")

        # --- Duplicate detection via content hash -------------------------
        raw_bytes = staged_path.read_bytes()
        content_hash = hashlib.sha256(raw_bytes).hexdigest()

        if database.hash_exists(conn, content_hash):
            logger.info("Duplicate detected for job %d, skipping", job_row["id"])
            return ("skipped", None, 0)

        # --- Persist document, chunks, and embeddings ---------------------
        title = job_row["title"] or filename
        doc_id = database.insert_document(
            conn,
            title=title,
            source_path=str(staged_path),
            content_hash=content_hash,
            doc_type=doc_type,
            language=language,
        )

        chunk_texts = [c if isinstance(c, str) else c["text"] for c in chunks]
        chunk_metas = []
        for idx, c in enumerate(chunks):
            if isinstance(c, str):
                chunk_metas.append(None)
            else:
                meta = {k: v for k, v in c.items() if k != "text"} or None
                chunk_metas.append(meta)

        enriched_texts = [
            build_enriched_text(title, ct, cm)
            for ct, cm in zip(chunk_texts, chunk_metas)
        ]
        vectors = embeddings.embed_texts(enriched_texts)

        for idx, (chunk_text, enriched, vector) in enumerate(
            zip(chunk_texts, enriched_texts, vectors)
        ):
            chunk_id = database.insert_chunk(
                conn,
                document_id=doc_id,
                chunk_index=idx,
                text=chunk_text,
                enriched_text=enriched,
                metadata=chunk_metas[idx],
            )
            database.insert_embedding(conn, chunk_id, vector)

        # --- Tags ---------------------------------------------------------
        tags = json.loads(job_row["tags_json"] or "[]")
        if tags:
            database.tag_document(conn, doc_id, tags)

        conn.commit()

        # --- Move original file to persistent storage ---------------------
        ext = Path(filename).suffix or staged_path.suffix
        dest = cfg.documents_dir / f"{content_hash}{ext}"
        try:
            cfg.documents_dir.mkdir(parents=True, exist_ok=True)
            shutil.move(str(staged_path), str(dest))
            conn_update = database.get_connection(cfg.db_path)
            try:
                conn_update.execute(
                    "UPDATE documents SET stored_path = ?, original_filename = ? WHERE id = ?",
                    (str(dest), filename, doc_id),
                )
                conn_update.commit()
            finally:
                conn_update.close()
            logger.info("Stored original file: %s", dest)
        except Exception as exc:
            logger.warning("Failed to store original file: %s", exc)
            staging.cleanup(staged_path)

        return ("done", doc_id, len(chunk_texts))

    finally:
        conn.close()
        # Only clean up staging if the file is still there (not moved)
        if staged_path.exists():
            staging.cleanup(staged_path)